nnet

examples/meetup/nnet.ipynb

@@ -1,5 +1,223 @@
 {
  "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "plotgrid (generic function with 1 method)"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "using Plots; qwt()\n",
+    "default(size=(500,300), leg=false)\n",
+    "\n",
+    "# creates x/y vectors which can define a grid in a zig-zag pattern\n",
+    "function gridxy(lim, n::Int)\n",
+    "    xs = linspace(lim..., n)\n",
+    "    xypairs = vec([(x,y) for x in vcat(xs,reverse(xs)), y in xs])\n",
+    "    Plots.unzip(xypairs)\n",
+    "end\n",
+    "\n",
+    "# plot a grid from x/y vectors\n",
+    "function plotgrid(x, y)\n",
+    "    plot([x y], [y x], c=:black)\n",
+    "end"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# The problem... can we classify the functions?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "f2 (generic function with 1 method)"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# these are the functions we want to classify\n",
+    "f1(x) = 0.6sin(10x) + 0.1\n",
+    "f2(x) = f1(x) - 0.2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Build a neural net"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 941,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "using OnlineAI\n",
+    "\n",
+    "# first create a neural net to separate the functions\n",
+    "numInputs = 2\n",
+    "numOutputs = 1\n",
+    "hiddenLayerStructure = [3,3,2]\n",
+    "net = buildClassificationNet(numInputs, numOutputs, hiddenLayerStructure; hiddenActivation = TanhActivation())\n",
+    "\n",
+    "# show the network\n",
+    "viz = visualize(net);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# On to the fun..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 942,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# pick the plotting limits\n",
+    "lim = (-1,1)\n",
+    "default(xlim = lim, ylim = lim)\n",
+    "\n",
+    "# show the grid\n",
+    "n = 40\n",
+    "gridx, gridy = gridxy(lim, n)\n",
+    "p = plotgrid(gridx, gridy)\n",
+    "\n",
+    "# show the funcs\n",
+    "funcs = [f1, f2]\n",
+    "plot!(funcs, lim..., w=3)\n",
+    "\n",
+    "# kick off an animation... we can save frames whenever we want, lets save the start\n",
+    "anim = Animation()\n",
+    "frame(anim)\n",
+    "\n",
+    "# open a gui window\n",
+    "gui()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Yikes... that looks tricky to separate..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 945,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "activateHidden (generic function with 1 method)"
+      ]
+     },
+     "execution_count": 945,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# function to sample from x's\n",
+    "xsample() = rand(Distributions.Uniform(lim...)) \n",
+    "\n",
+    "# pick one of the functions at random, sample from the x line, then update the\n",
+    "# neural net with [x, f(x)] as the inputs\n",
+    "function sampleAndUpdate()\n",
+    "    f = sample(funcs)\n",
+    "    x = xsample()\n",
+    "    y = float(f == f1)\n",
+    "    update!(net, Float64[x, f(x)], [y])\n",
+    "end\n",
+    "\n",
+    "# take x matrix and convert to the first layer's activation\n",
+    "function activateHidden(net, x)\n",
+    "    input = x\n",
+    "    for layer in net.layers[1:end-1]\n",
+    "        proj = Array(nrows(x), layer.nout)\n",
+    "        for i in 1:nrows(x)\n",
+    "            OnlineAI.forward!(layer, row(proj,i), false)\n",
+    "            row!(proj, i, layer.a)\n",
+    "        end\n",
+    "        input = proj\n",
+    "    end\n",
+    "    vec(proj[:,1]), vec(proj[:,2])\n",
+    "end    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 946,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "LoadError",
+     "evalue": "LoadError: MethodError: `convert` has no method matching convert(::Type{Array{T,N}}, ::Int64, ::Int64)\nThis may have arisen from a call to the constructor Array{T,N}(...),\nsince type constructors fall back to convert methods.\nClosest candidates are:\n  convert{T,N}(::Type{Array{T,N}}, !Matched::DataArrays.DataArray{T,N}, ::Any)\n  convert{T,R,N}(::Type{Array{T,N}}, !Matched::DataArrays.PooledDataArray{T,R,N}, ::Any)\n  Array{T}(!Matched::Type{T}, ::Integer)\n  ...\nwhile loading In[946], in expression starting on line 8",
+     "output_type": "error",
+     "traceback": [
+      "LoadError: MethodError: `convert` has no method matching convert(::Type{Array{T,N}}, ::Int64, ::Int64)\nThis may have arisen from a call to the constructor Array{T,N}(...),\nsince type constructors fall back to convert methods.\nClosest candidates are:\n  convert{T,N}(::Type{Array{T,N}}, !Matched::DataArrays.DataArray{T,N}, ::Any)\n  convert{T,R,N}(::Type{Array{T,N}}, !Matched::DataArrays.PooledDataArray{T,R,N}, ::Any)\n  Array{T}(!Matched::Type{T}, ::Integer)\n  ...\nwhile loading In[946], in expression starting on line 8",
+      "",
+      " in activateHidden at In[945]:17"
+     ]
+    }
+   ],
+   "source": [
+    "# update net with new samples\n",
+    "for i in 1:10000\n",
+    "    sampleAndUpdate()\n",
+    "end\n",
+    "\n",
+    "# update the plot... project each series to the first hidden layer and reset the data\n",
+    "x = linspace(lim..., 100)\n",
+    "p[1] = activateHidden(net, hcat(gridx, gridy))\n",
+    "p[2] = activateHidden(net, hcat(gridy, gridx))\n",
+    "p[3] = activateHidden(net, hcat(x, map(f1,x)))\n",
+    "p[4] = activateHidden(net, hcat(x, map(f2,x)))\n",
+    "\n",
+    "# show/update the plot\n",
+    "gui(p)\n",
+    "frame(anim);"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -8,30 +226,20 @@
    },
    "outputs": [],
    "source": [
-    "using Plots, DataFrames, OnlineStats, OnlineAI\n",
-    "default(size=(500,300))\n",
-    "df = readtable(joinpath(Pkg.dir(\"Plots\"), \"examples\", \"meetup\", \"winequality-white.csv\"), separator=';');"
+    "# build an animated gif\n",
+    "gif(anim, fps = 10)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 940,
    "metadata": {
-    "collapsed": false,
-    "scrolled": false
+    "collapsed": false
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(xmeta,ymeta) = (nothing,nothing)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "using Plots; gadfly()\n",
-    "p = plot(10);"
+    "# update the net representation with weights, etc\n",
+    "update!(viz)"
    ]
   },
   {
@@ -61,6 +269,51 @@
    "outputs": [],
    "source": []
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,

test/imgcomp.jl

@@ -133,7 +133,7 @@ function image_comparison_tests(pkg::Symbol, idx::Int; debug = false, sigma = [1
     return true
 
   catch ex
-    warn("Image did not match reference image $reffn")
+    warn("Image did not match reference image $reffn. err: $ex")
     if isinteractive()
 
       # if we're in interactive mode, open a popup and give us a chance to examine the images